arXiv subjects

Glenn Lawyer

Publications and source records attributed to Glenn Lawyer.

4 recordsLinked to original sources

arXiv2015

Technical Report: Performance of the Expected Force on AS-level Inernet topologies

Many concepts in network science are based on idealized network models. Infrastructure networks, however, are strongly constrained by specific engineering and economic constraints which do not appear in such models. One result is that concepts which are assumed to be fundamental by theoriticians severely underperform in real world situations. This motivated us to test if the Expected Force (ExF) metric, which was developed in the context of more theoretical network models, would continue to be predictive of epidemic outcomes on the highly structured topology of the internet at the autonomous systems (AS) level router connectivity. Results suggest that the ExF performs extremely well on these topologies, perhaps reflecting their design constraint of easing the diffusion of information.

cs.NI↗

arXiv2015

Stochasticity in pandemic spread over the World Airline Network explained by local flight connections

Massive growth in human mobility has dramatically increased the risk and rate of pandemic spread. Macro-level descriptors of the topology of the World Airline Network (WAN) explains middle and late stage dynamics of pandemic spread mediated by this network, but necessarily regard early stage variation as stochastic. We propose that much of early stage variation can be explained by appropriately characterizing the local topology surrounding the debut location of an outbreak. We measure for each airport the expected force of infection (AEF) which a pandemic originating at that airport would generate. We observe, for a subset of world airports, the minimum transmission rate at which a disease becomes pandemically competent at each airport. We also observe, for a larger subset, the time until a pandemically competent outbreak achieves pandemic status given its debut location. Observations are generated using a highly sophisticated metapopulation reaction-diffusion simulator under a disease model known to well replicate the 2009 influenza pandemic. The robustness of the AEF measure to model misspecification is examined by degrading the network model. AEF powerfully explains pandemic risk, showing correlation of 0.90 to the transmission level needed to give a disease pandemic competence, and correlation of 0.85 to the delay until an outbreak becomes a pandemic. The AEF is robust to model misspecification. For 97% of airports, removing 15% of airports from the model changes their AEF metric by less than 1%. Appropriately summarizing the size, shape, and diversity of an airport's local neighborhood in the WAN accurately explains much of the macro-level stochasticity in pandemic outcomes.

cs.SI↗

arXiv2014

Understanding the spreading power of all nodes in a network: a continuous-time perspective

Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the \textit{expected force}, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large.

cs.SI↗

arXiv2012

Measuring node spreading power by expected cluster degree

Traditional metrics of node influence such as degree or betweenness identify highly influential nodes, but are rarely usefully accurate in quantifying the spreading power of nodes which are not. Such nodes are the vast majority of the network, and the most likely entry points for novel influences, be they pandemic disease or new ideas. Several recent works have suggested metrics based on path counting. The current work proposes instead using the expected number of infected-susceptible edges, and shows that this measure predicts spreading power in discrete time, continuous time, and competitive spreading processes simulated on large random networks and on real world networks. Applied to the Ugandan road network, it predicts that Ebola is unlikely to pose a pandemic threat.

cs.SI↗